Structural feature selection for event logs

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • QPR Software Plc

Abstract

We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms’ run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection.

Details

Original languageEnglish
Title of host publicationBusiness Process Management Workshops - BPM 2017 International Workshops, Revised Papers
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Business Process Management - Sydney, Australia
Duration: 9 Sep 201814 Sep 2018
Conference number: 16

Publication series

NameLecture Notes in Business Information Processing
Volume308
ISSN (Print)1865-1348

Workshop

WorkshopInternational Conference on Business Process Management
Abbreviated titleBPM
CountryAustralia
CitySydney
Period09/09/201814/09/2018

    Research areas

  • Automatic business process discovery, Classification, Clustering, Feature selection, Machine learning, Prediction, Process mining

ID: 17832384